
We propose D-RISE, a black-box attribution technique for explaining object detectors via saliency maps, by significantly extending the idea of exhaustively and systematically exploring areas that affect the prediction outputs of the underlying model by manipulating its inputs.

We demonstrate D-RISE for explaining two commonly used object detectors with different architectural designs, namely one-stage YOLOv3 [1]} and two-stage Faster R-CNN [2]}.

We systematically analyze using D-RISE potential sources of errors and bias in commonly used object detectors trained on the COCO dataset and discovering common patterns in data picked up by the model.

We propose additional evaluation by producing synthetically modified datasets by inserting markers that deliberately introduce biases in the dataset that are effectively discovered by D-RISE.

